基于深度学习的军事目标识别算法综述

目标识别作为深度学习中最受欢迎的领域之一，已广泛应用于民用的各个方面，如人脸识别、行人重识别、车牌识别、车辆识别等；而在军事应用领域，由于军事目标数据集较少，但识别要求精度高实时性强，所以还在发展阶段。本文首先阐述了基于深度学习的军事目标识别发展现状；然后介绍了六种目前主流的基于深度学习的军事目标识别算法（包括Mask R-CNN、GAN与深度森林、DRFCN、E-MobileNet、SSD300、YOLO）及相关网络结构、改进方法与实际应用；最后对主流方法进行总结，并探讨了未来的发展趋势。

Overview of military target recognition algorithms based on Deep Learning

As one of the most popular fields in deep learning, target recognition has been widely applied for civil use, such as face recognition, pedestrian recognition, license plate recognition, vehicle recognition and so on; In the field of military application, it is in its development stage given that there are few military target data sets, but the recognition requires high precision and strong real-time performance. Firstly, the development status of military target recognition was expounded based on deep learning; Then, six mainstream military target recognition algorithms are introduced on the basis of deep learning (including Mask R-CNN, GAN and Deep Forest, DRFCN, E-MobileNet, SSD300 and YOLO) and their related network structures, improvement methods and practical applications; Finally, the mainstream methods are summarized, and the future development trend is discussed.